Figure 1: Arithmetic means of crop and weed biomass.
Crop biomass was the strongest factor affecting weed biomass (Table 2 so a non-linear regression was fitted for crop - weed competition (Figure 2). Collard’s weed suppression was distinctive among all the treatments (Table 4.
A nonlinear regression was fitted with \(Y = \frac{C}{1 + I_w B_c}\), where
\(Y\) is predicted weed biomass,
\(C\) is weed biomass in the control treatment,
\(I_w\) is an estimated coefficient,
\(B_c\) is crop biomass in cover crop treatment
##
## Formula: weed.biomass.g.per.sq.m ~ 148/(1 + i.w * crop.biomass.g.per.sq.m)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## i.w 0.008078 0.003155 2.561 0.0137 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 75.39 on 47 degrees of freedom
##
## Number of iterations to convergence: 6
## Achieved convergence tolerance: 3.688e-06
Figure 2: Weed suppression from Brassicaceae biomass
Figure 3: Weed suppression from Brassicaceae biomass
Figure 4: Weed suppression from Brassicaceae biomass
Weed suppression from different Brassicacaea species was comparable among each other and with the control treatments (Table 1), even though crop biomass differed by species (Table 2).
Using ggResidpanel version 0.3.0 (Goode and Rey, 2019) for model diagnosis, no predictable pattern in the plots of residuals vs. predicted values suggests that the analysis models fit the data well (Figures ?? and 7), but AIC values of the ANCOVA models were lower than those of the ANOVA models: 448 vs. 459 for crop responses in ANCOVA vs. ANOVA; and 159 vs. 174 for weed biomass responses in ANCOVA vs ANOVA.
fsb.crops <- fsb.wide %>% filter(treatment != "N") # Control treatments had no crop
crops.lm1 <- lm(crop.biomass.g.per.sq.m ~ as.factor(block) + species.y + weed.biomass.g.per.sq.m, data = fsb.crops)
crops.lm2 <- lm(crop.biomass.g.per.sq.m ~ as.factor(block) + species.y , data = fsb.crops)
resid_panel(crops.lm2)
Figure 5: Diagnosis plot for the ANCOVA and ANOVA models of crop biomass responses
list(crops.lm1, crops.lm2) %>%
lapply(resid_panel, nrow = 2) %>%
wrap_plots() +
plot_annotation(tag_levels = "A")